def generate_mf_group(self, G, x): mf_group = {} for (k, v) in G.iteritems(): shp = v['shp'] mf = v['mf'] if mf == 'trap': mf_group[k] = trapmf(x, shp) if mf == 'tri': mf_group[k] = trimf(x, shp) if mf == 'gbell': mf_group[k] = gbellmf(x, shp[0], shp[1], shp[2]) if mf == 'gauss': mf_group[k] = gaussmf(x, shp[0], shp[1]) if mf == 'gauss2': mf_group[k] = gauss2mf(x, shp[0], shp[1]) if mf == 'sig': mf_group[k] = sigmf(x, shp[0], shp[1]) if mf == 'psig': mf_group[k] = psigmf(x, shp[0], shp[1], shp[2], shp[3]) if mf == 'zmf': mf_group[k] = zmf(x, shp[0], shp[1], shp[2], shp[3]) if mf == 'smf': mf_group[k] = smf(x, shp[0], shp[1], shp[2], shp[3]) if mf == 'pimf': mf_group[k] = pimf(x, shp[0], shp[1], shp[2], shp[3]) if mf == 'piecemf': mf_group[k] = piecemf(x, shp[0], shp[1], shp[2], shp[3]) return mf_group
def test_psigmf(): x = np.arange(-4, 4.1, 0.1) b1, c1, b2, c2 = -1.75, -np.pi / 2., 0.972, 0.43 expected = ((1 / (1. + np.exp(- c1 * (x - b1)))) * (1 / (1. + np.exp(- c2 * (x - b2))))) test = psigmf(x, b1, c1, b2, c2) assert_allclose(test, expected)
def test_psigmf(): x = np.arange(-4, 4.1, 0.1) b1, c1, b2, c2 = -1.75, -np.pi / 2., 0.972, 0.43 expected = ((1 / (1. + np.exp(-c1 * (x - b1)))) * (1 / (1. + np.exp(-c2 * (x - b2))))) test = psigmf(x, b1, c1, b2, c2) assert_allclose(test, expected)